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The Dynamic Relationship between Macroeconomy and Stock Market in China: Evidence from Bayesian Network

Complexity 2021年第1期2021卷

作者： Liu, Yue Feng, Haoyuan Guo, Kun School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

As the most important component of the capital market, the stock market has always been regarded as the "barometer"of the macroeconomy. However, many researchers have found that the stock market and macroeconomy are operating separately. This paper uses the dynamic Bayesian network method to study the dynamic relationship between the Chinese macroeconomic system and the stock market. The study found that the correlation between the macroeconomic system and the stock market is not consistent in different time periods. For most of the time, the stock system and the macroeconomic system are relatively independent. However, several macroeconomic factors such as Purchase Management Index could affect the stock market through some industries. A conclusion is drawn that the "barometer"function of the stock market is weak and easy to be damaged by factors such as the irrational sentiment of investors. © 2021 Yue Liu et al.

关键词： Financial markets

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Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and Outlook

arXiv

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arXiv 2024年

作者： Song, Ziying Liu, Lin Jia, Feiyang Luo, Yadan Jia, Caiyan Zhang, Guoxin Yang, Lei Wang, Li School of Computer Science & Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China School of Information Technology and Electrical Engineering The University of Queensland St LuciaQLD4072 Australia School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Intelligent Green Vehicle and Mobility The School of Vehicle and Mobility Tsinghua University Beijing100084 China School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China

In the realm of modern autonomous driving, the perception system is indispensable for accurately assessing the state of the surrounding environment, thereby enabling informed prediction and planning. The key step to this system is related to 3D object detection that utilizes vehicle-mounted sensors such as LiDAR and cameras to identify the size, the category, and the location of nearby objects. Despite the surge in 3D object detection methods aimed at enhancing detection precision and efficiency, there is a gap in the literature that systematically examines their resilience against environmental variations, noise, and weather changes. This study emphasizes the importance of robustness, alongside accuracy and latency, in evaluating perception systems under practical scenarios. Our work presents an extensive survey of camera-only, LiDAR-only, and multimodal 3D object detection algorithms, thoroughly evaluating their trade-off between accuracy, latency, and robustness, particularly on datasets like KITTI-C and nuScenes-C to ensure fair comparisons. Among these, multi-modal 3D detection approaches exhibit superior robustness, and a novel taxonomy is introduced to reorganize the literature for enhanced clarity. This survey aims to offer a more practical perspective on the current capabilities and the constraints of 3D object detection algorithms in real-world applications, thus steering future research towards robustness-centric advancements. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Modeling and Simulation for Battery Energy Storage System Participating in Power System Frequency Modulation Based on Power System Analysis Software Package 1

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Cao, Pengcheng Li, Peiqiang Sun, Peidong Zhang, Zhongkai Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Kota Kinabalu Sabah Malaysia

ISBN: (数字)9789813364202

ISBN: (纸本)9789813364196

Battery energy storage technology, with its fast and accurate power response, has become the focus of the auxiliary means of power system frequency modulation. However, the traditional simulation software lacks an accurate battery energy storage system component model, which affects the accuracy of analyzing the response characteristics of the energy storage system to a certain extent. This paper presents an electromechanical transient model of battery energy storage system without time delay, which considers the participation of energy storage system in frequency modulation dead zone and battery charging and discharging power. The control model is built based on the node current injection method by using the power system analysis integrated program, and the EPRI-7 standard system is selected for simulation analysis. The simulation results show that the model has good implant ability. And when the load is cut off in the power system, the energy storage system participating in frequency modulation can effectively maintain the frequency stability. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer software

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Quaternion factorization machines: A lightweight solution to intricate feature interaction modelling

arXiv

引用

arXiv 2021年

作者： Chen, Tong Yin, Hongzhi Zhang, Xiangliang Huang, Zi Wang, Yang Wang, Meng The School of Information Technology and Electrical Engineering The University of Queensland Australia The Machine Intelligence and Knowledge Engineering Laboratory King Abdullah University of Science and Technology Saudi Arabia The Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology China

Due to the sparsity of available features in web-scale predictive analytics, combinatorial features become a crucial means for deriving accurate predictions. As a well-established approach, factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering. With the prominent development of deep neural networks (DNNs), there is a recent and ongoing trend of enhancing the expressiveness of FM-based models with DNNs. However, though better results are obtained with DNN-based FM variants, such performance gain is paid off by an enormous amount (usually millions) of excessive model parameters on top of the plain FM. Consequently, the heavy parameterization impedes the real-life practicality of those deep models, especially efficient deployment on resource-constrained IoT and edge devices. In this paper, we move beyond the traditional real space where most deep FM-based models are defined, and seek solutions from quaternion representations within the hypercomplex space. Specifically, we propose the quaternion factorization machine (QFM) and quaternion neural factorization machine (QNFM), which are two novel lightweight and memory-efficient quaternion-valued models for sparse predictive analytics. By introducing a brand new take on FM-based models with the notion of quaternion algebra, our models not only enable expressive inter-component feature interactions, but also significantly reduce the parameter size due to lower degrees of freedom in the hypercomplex Hamilton product compared with real-valued matrix multiplication. Extensive experimental results on three large-scale datasets demonstrate that QFM achieves 4.36% performance improvement over the plain FM without introducing any extra parameters, while QNFM outperforms all baselines with up to two magnitudes' parameter size reduction in comparison to state-of-the-art peer methods. Copyright © 2021, The Authors. All

关键词： Large dataset

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Does the emotional tendency of COVID-19 news affect financial markets?

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Procedia Computer Science 2022年 199卷 532-539页

作者： Wen Long Yanqiang Zhong Ruiqin Li Manyi Zhao School of Economics & Management University of Chinese Academy of Sciences Beijing 100190 P.R. China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 P.R. China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 P.R. China Sino-Danish College University of Chinese Academy of Sciences Beijing 100049 P.R. China

The influence of internet financial news on financial markets has been verified from multiple perspectives. This paper examines whether the emotional tendency of COVID-19 news affect financial markets. Based on constructing the sentiment dictionary of COVID-19 news, we measure the sentiment indicators of COVID-19 news, and then study the dynamic relationship between the emotional tendency of COVID-19 news and the rate of return in financial markets. Compared with the news before the outbreak of COVID-19, our results show that the emotional tendency of COVID-19 news does affect the rate of return in the stock and bond markets.

关键词： COVID-19 news Emotional tendency Financial markets Semantic orientation

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Intervention Prediction for Patients with Pressure Injury Using Random Forest 12

Intervention Prediction for Patients with Pressure Injury Us...

引用

12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Jin, Liuqi Pan, Yan Yang, Jiaoyun Han, Lin Lv, Lin Raviv, Miki An, Ning Hefei University of Technology Key Laboratory of Knowledge Engineering With Big Data of The Ministry of Education School of Computer Science and Information Engineering Hefei China Lanzhou University Evidence-Based Nursing Center School of Nursing Lanzhou China Wound and Ostomy Care Center Gansu Provincal Hospital Lanzhou China Vitalerter LTD Ha-Yarden Airport City Israel

ISBN: (纸本)9781665438582

Pressure injury (PI) is one of the major causes of short-term death. Early intervention for patients at risk plays an essential role in PI. However, many nurses may ignore risks. This paper aims to establish a model to predict interventions according to the patient's physical signs, which can help nurses develop care plans. We used data from 1, 483 patients with 25 characteristics and 17 interventions. We use the Random Forest and Particle Swarm Optimization (PSO) to optimize model parameters. Then we compared it with KNN, SVM, and Decision Tree. The 10-fold cross-validation result showed that the Random Forest has better accuracy than other methods, with an f1 score of 0.84. This finding proved the feasibility of using machine learning to help formulate care plans according to the classification of index prediction results. Our model shows that hemoglobin, Braden PI score, and age are the three most influential risk factors. © 2021 IEEE.

关键词： Decision trees

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Investors pay attention to multi-level financial news ——"A pure waste of time" or "It’s worth it"

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Procedia Computer Science 2022年 199卷 415-422页

作者： Wen Long Jiaqi Tian Yuanfeng Mao Yuan An School of Economics & Management University of Chinese Academy of Sciences Beijing 100190 P.R. China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 P.R. China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 P.R. China Sino-Danish College University of Chinese Academy of Sciences Beijing 100049 P.R. China

In recent years,the efficiency of news communication has been greatly improved with the advance of Internet technology, and whether investors should pay attention to multiple levels of news when making trading decisions is worth pondering. However, Chinese scholars have paid little attention to the news hierarchy, and do not bring different levels of news into the prediction system. Therefore, this paper studies the stock price trend prediction from this perspective. This paper selects the medical industry as the representative, and adds its sub industry news and industry news to form a multi-level news system on the basis of individual stock news. In order to make better use of and extract the information contained in different levels of news, the multi-kernel learning model is incorporated on the basis of using SVM. The results suggest that the use of multi-level news can improve the accuracy of stock price trend prediction. Compared with the SVM model solely considering individual stock news, the prediction accuracy of multi-kernel learning model based on multi-level news is improved by 10%, which is of great significance for investment decision-making.

关键词： stock price trend multi-level financial news multi-kernel learning model

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Hexagonally ordered microbowl arrays decorated with ultrathin CuInS_(2) nanosheets for enhanced photoelectrochemical performance

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Journal of Energy Chemistry 2020年第12期29卷 134-142页

作者： Ming Li Le Chen Yanjie Su Huan Yin Kexiang Hu State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyKey Laboratory of Optoelectronic Information Processing of Guangxi Colleges and Uniy/ersitiesGuilin 541004GuangxiChina Key Laboratory for Thin Film and Microfabrication of the Ministry of Education Department of Micro/Nano ElectronicsSchool of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China National and Local Joint Engineering Laboratory of Internet Technology on Mine School of Information and Control EngineeringChina University of Mining and TechnologyXuzhou 221008JiangsuChina School of Physics and Telecommunication Engineering Guangxi Colleges and Universities Key Lab of Complex System Optimization and Big Data ProcessingYulin Normal UniversityYulin 537400GuangxiChina

This paper demonstrates the design and fabrication of three-dimensional(3 D) hexagonally ordered microbowl arrays(MBAs) decorated with Cu In S2 nanosheets for enhanced photoelectrochemical(PEC) performance. The 3 D MBAs are fabricated by a micro-fabrication technique. The ultrathin CuInS2 nanosheets are grown on the 3 D electrodes by solvothermal transformation of Cu film. The photocurrent density of 3 D photocathode(CuInS2@MBAs) is about two times higher than that of the planar counterpart(CuInS2@Planar). The improved PEC performance can be ascribed to the elevated light trapping ability and the increased surface area for loading photocatalysts. In addition, CdS quantum dots as cocatalysts are modified onto the Cu In S2 nanosheets to further enhance the PEC activity because the formed p-n heterojunction can accelerate the separation of photogenerated carriers. As a result, the 3 D photocathode of CuInS2/CdS@MBAs shows an optimal incident photon to current efficiency of 10% at the wavelength of400 nm. It is believed that this work can be generalized to design other hierarchical 3 D photoelectrodes for improved solar water splitting.

关键词： Microbowl array CuInS_(2) Nanosheet Photocathode Photoelectrochemical performance

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Traffic engineering in Dynamic Hybrid Segment Routing Networks

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Computers, Materials & Continua 2021年第7期68卷 655-670页

作者： Yingya Guo Kai Huang Cheng Hu Jiangyuan Yao Siyu Zhou College of Mathematics and Computer Science Fuzhou UniversityFuzhou350000China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350000China Key Laboratory of Spatial Data Mining&Information Sharing Ministry of EducationFuzhou350003China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science and Cyberspace Security Hainan UniversityHaiKou570228China Tandon School of Engineering New York UniversityNew York10012USA Department of Computing Hong Kong Polytechnic UniversityHong Hom999077Hong Kong

The emergence of Segment Routing(SR)provides a novel routing paradigm that uses a routing technique called source packet *** SR architecture,the paths that the packets choose to route on are indicated at the ingress *** with shortest-path-based routing in traditional distributed routing protocols,SR can realize a flexible routing by implementing an arbitrary flow splitting at the ingress *** the advantages of SR,it may be difficult to update the existing IP network to a full SR deployed network,for economical and technical *** partial of the traditional IP network to the SR network,thus forming a hybrid SR network,is a preferable *** the traffic is dynamically changing in a daily time,in this paper,we propose a Weight Adjustment algorithm WASAR to optimize routing in a dynamic hybrid SR *** algorithm can be divided into three steps:firstly,representative Traffic Matrices(TMs)and the expected TM are obtained from the historical TMs through ultrascalable spectral clustering ***,given the network topology,the initial network weight setting and the expected TM,we can realize the link weight optimization and SR node deployment optimization through a Deep Reinforcement Learning(DRL)***,we optimize the flow splitting ratios of SR nodes in a centralized online manner under dynamic traffic demands,in order to improve the network *** the evaluation,we exploit historical TMs to test the performance of the obtained routing configuration in *** extensive experimental results validate that our proposed WASAR algorithm has superior performance in reducing Maximum Link Utilization(MLU)under the dynamic traffic.

关键词： Traffic engineering routing optimization segment routing deep reinforcement learning ultra-scalable spectral clustering

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New entanglement-assisted quantum codes from negacyclic codes

arXiv

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arXiv 2023年

作者： Chen, Xiaojing Lu, Xingbo Zhu, Shixin Jiang, Wan Wang, Xindi School of Internet Anhui University Anhui Hefei230039 China School of Mathematics Hefei University of Technology Anhui Hefei230601 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Anhui Hefei230601 China School of Computer and Information Hefei University of Technology Anhui Hefei230601 China

The theory of entanglement-assisted quantum error-correcting codes (EAQECCs) is a generalization of the standard stabilizer quantum error-correcting codes, which can be possibly constructed from any classical codes by relaxing the duality condition and utilizing preshared entanglement between the sender and receiver. In this paper, a new family of EAQECCs is constructed from negacyclic codes of length n = q2a+1, where q is an odd prime power, a = m22+1 and m is an odd integer. Some new entanglement-assisted quantum maximum distance separable (EAQMDS) codes are obtained in the sense that their parameters are not covered by the previously known ones. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum entanglement

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